In [1]:
C:\Users\ASUS\anaconda3\Lib\site-packages\pandas\core\arrays\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).
  from pandas.core import (
In [2]:
                         State_Name District_Name  Crop_Year       Season  \
0       Andaman and Nicobar Islands      NICOBARS       2000  Kharif        
1       Andaman and Nicobar Islands      NICOBARS       2000  Kharif        
2       Andaman and Nicobar Islands      NICOBARS       2000  Kharif        
3       Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    
4       Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    
...                             ...           ...        ...          ...   
246086                  West Bengal       PURULIA       2014  Summer        
246087                  West Bengal       PURULIA       2014  Summer        
246088                  West Bengal       PURULIA       2014  Whole Year    
246089                  West Bengal       PURULIA       2014  Winter        
246090                  West Bengal       PURULIA       2014  Winter        

                       Crop      Area  Production  
0                  Arecanut    1254.0      2000.0  
1       Other Kharif pulses       2.0         1.0  
2                      Rice     102.0       321.0  
3                    Banana     176.0       641.0  
4                 Cashewnut     720.0       165.0  
...                     ...       ...         ...  
246086                 Rice     306.0       801.0  
246087              Sesamum     627.0       463.0  
246088            Sugarcane     324.0     16250.0  
246089                 Rice  279151.0    597899.0  
246090              Sesamum     175.0        88.0  

[246091 rows x 7 columns]
In [3]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 246091 entries, 0 to 246090
Data columns (total 7 columns):
 #   Column         Non-Null Count   Dtype  
---  ------         --------------   -----  
 0   State_Name     246091 non-null  object 
 1   District_Name  246091 non-null  object 
 2   Crop_Year      246091 non-null  int64  
 3   Season         246091 non-null  object 
 4   Crop           246091 non-null  object 
 5   Area           246091 non-null  float64
 6   Production     242361 non-null  float64
dtypes: float64(2), int64(1), object(4)
memory usage: 13.1+ MB
Out[3]:
(246091, 7)
In [4]:
Out[4]:
State_Name District_Name Crop_Year Season Crop Area Production
0 Andaman and Nicobar Islands NICOBARS 2000 Kharif Arecanut 1254.0 2000.0
1 Andaman and Nicobar Islands NICOBARS 2000 Kharif Other Kharif pulses 2.0 1.0
2 Andaman and Nicobar Islands NICOBARS 2000 Kharif Rice 102.0 321.0
3 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Banana 176.0 641.0
4 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Cashewnut 720.0 165.0
5 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Coconut 18168.0 65100000.0
6 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Dry ginger 36.0 100.0
7 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Sugarcane 1.0 2.0
8 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Sweet potato 5.0 15.0
9 Andaman and Nicobar Islands NICOBARS 2000 Whole Year Tapioca 40.0 169.0
In [5]:
Out[5]:
State_Name District_Name Crop_Year Season Crop Area Production
246081 West Bengal PURULIA 2014 Rabi Rapeseed &Mustard 1885.0 1508.0
246082 West Bengal PURULIA 2014 Rabi Safflower 54.0 37.0
246083 West Bengal PURULIA 2014 Rabi Urad 220.0 113.0
246084 West Bengal PURULIA 2014 Rabi Wheat 1622.0 3663.0
246085 West Bengal PURULIA 2014 Summer Maize 325.0 2039.0
246086 West Bengal PURULIA 2014 Summer Rice 306.0 801.0
246087 West Bengal PURULIA 2014 Summer Sesamum 627.0 463.0
246088 West Bengal PURULIA 2014 Whole Year Sugarcane 324.0 16250.0
246089 West Bengal PURULIA 2014 Winter Rice 279151.0 597899.0
246090 West Bengal PURULIA 2014 Winter Sesamum 175.0 88.0
In [6]:
Out[6]:
State_Name          0
District_Name       0
Crop_Year           0
Season              0
Crop                0
Area                0
Production       3730
dtype: int64
In [6]:
C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\2013743738.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  dataset['Production'].fillna(dataset['Production'].mean(),inplace = True) #inplace= True is used to change the values directly in the dataframe itself
Out[6]:
State_Name       0
District_Name    0
Crop_Year        0
Season           0
Crop             0
Area             0
Production       0
dtype: int64
In [7]:
Out[7]:
State_Name          33
District_Name      646
Crop_Year           19
Season               6
Crop               124
Area             38442
Production       51628
dtype: int64
In [8]:
Out[8]:
array(['Andaman and Nicobar Islands', 'Andhra Pradesh',
       'Arunachal Pradesh', 'Assam', 'Bihar', 'Chandigarh',
       'Chhattisgarh', 'Dadra and Nagar Haveli', 'Goa', 'Gujarat',
       'Haryana', 'Himachal Pradesh', 'Jammu and Kashmir ', 'Jharkhand',
       'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',
       'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Puducherry',
       'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu', 'Telangana ',
       'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal'],
      dtype=object)
In [9]:
['NICOBARS' 'NORTH AND MIDDLE ANDAMAN' 'SOUTH ANDAMANS' 'ANANTAPUR'
 'CHITTOOR' 'EAST GODAVARI' 'GUNTUR' 'KADAPA' 'KRISHNA' 'KURNOOL'
 'PRAKASAM' 'SPSR NELLORE' 'SRIKAKULAM' 'VISAKHAPATANAM' 'VIZIANAGARAM'
 'WEST GODAVARI' 'ANJAW' 'CHANGLANG' 'DIBANG VALLEY' 'EAST KAMENG'
 'EAST SIANG' 'KURUNG KUMEY' 'LOHIT' 'LONGDING' 'LOWER DIBANG VALLEY'
 'LOWER SUBANSIRI' 'NAMSAI' 'PAPUM PARE' 'TAWANG' 'TIRAP' 'UPPER SIANG'
 'UPPER SUBANSIRI' 'WEST KAMENG' 'WEST SIANG' 'BAKSA' 'BARPETA'
 'BONGAIGAON' 'CACHAR' 'CHIRANG' 'DARRANG' 'DHEMAJI' 'DHUBRI' 'DIBRUGARH'
 'DIMA HASAO' 'GOALPARA' 'GOLAGHAT' 'HAILAKANDI' 'JORHAT' 'KAMRUP'
 'KAMRUP METRO' 'KARBI ANGLONG' 'KARIMGANJ' 'KOKRAJHAR' 'LAKHIMPUR'
 'MARIGAON' 'NAGAON' 'NALBARI' 'SIVASAGAR' 'SONITPUR' 'TINSUKIA'
 'UDALGURI' 'ARARIA' 'ARWAL' 'AURANGABAD' 'BANKA' 'BEGUSARAI' 'BHAGALPUR'
 'BHOJPUR' 'BUXAR' 'DARBHANGA' 'GAYA' 'GOPALGANJ' 'JAMUI' 'JEHANABAD'
 'KAIMUR (BHABUA)' 'KATIHAR' 'KHAGARIA' 'KISHANGANJ' 'LAKHISARAI'
 'MADHEPURA' 'MADHUBANI' 'MUNGER' 'MUZAFFARPUR' 'NALANDA' 'NAWADA'
 'PASHCHIM CHAMPARAN' 'PATNA' 'PURBI CHAMPARAN' 'PURNIA' 'ROHTAS'
 'SAHARSA' 'SAMASTIPUR' 'SARAN' 'SHEIKHPURA' 'SHEOHAR' 'SITAMARHI' 'SIWAN'
 'SUPAUL' 'VAISHALI' 'CHANDIGARH' 'BALOD' 'BALODA BAZAR' 'BALRAMPUR'
 'BASTAR' 'BEMETARA' 'BIJAPUR' 'BILASPUR' 'DANTEWADA' 'DHAMTARI' 'DURG'
 'GARIYABAND' 'JANJGIR-CHAMPA' 'JASHPUR' 'KABIRDHAM' 'KANKER' 'KONDAGAON'
 'KORBA' 'KOREA' 'MAHASAMUND' 'MUNGELI' 'NARAYANPUR' 'RAIGARH' 'RAIPUR'
 'RAJNANDGAON' 'SUKMA' 'SURAJPUR' 'SURGUJA' 'DADRA AND NAGAR HAVELI'
 'NORTH GOA' 'SOUTH GOA' 'AHMADABAD' 'AMRELI' 'ANAND' 'BANAS KANTHA'
 'BHARUCH' 'BHAVNAGAR' 'DANG' 'DOHAD' 'GANDHINAGAR' 'JAMNAGAR' 'JUNAGADH'
 'KACHCHH' 'KHEDA' 'MAHESANA' 'NARMADA' 'NAVSARI' 'PANCH MAHALS' 'PATAN'
 'PORBANDAR' 'RAJKOT' 'SABAR KANTHA' 'SURAT' 'SURENDRANAGAR' 'TAPI'
 'VADODARA' 'VALSAD' 'AMBALA' 'BHIWANI' 'FARIDABAD' 'FATEHABAD' 'GURGAON'
 'HISAR' 'JHAJJAR' 'JIND' 'KAITHAL' 'KARNAL' 'KURUKSHETRA' 'MAHENDRAGARH'
 'MEWAT' 'PALWAL' 'PANCHKULA' 'PANIPAT' 'REWARI' 'ROHTAK' 'SIRSA'
 'SONIPAT' 'YAMUNANAGAR' 'CHAMBA' 'HAMIRPUR' 'KANGRA' 'KINNAUR' 'KULLU'
 'LAHUL AND SPITI' 'MANDI' 'SHIMLA' 'SIRMAUR' 'SOLAN' 'UNA' 'ANANTNAG'
 'BADGAM' 'BANDIPORA' 'BARAMULLA' 'DODA' 'GANDERBAL' 'JAMMU' 'KARGIL'
 'KATHUA' 'KISHTWAR' 'KULGAM' 'KUPWARA' 'LEH LADAKH' 'POONCH' 'PULWAMA'
 'RAJAURI' 'RAMBAN' 'REASI' 'SAMBA' 'SHOPIAN' 'SRINAGAR' 'UDHAMPUR'
 'BOKARO' 'CHATRA' 'DEOGHAR' 'DHANBAD' 'DUMKA' 'EAST SINGHBUM' 'GARHWA'
 'GIRIDIH' 'GODDA' 'GUMLA' 'HAZARIBAGH' 'JAMTARA' 'KHUNTI' 'KODERMA'
 'LATEHAR' 'LOHARDAGA' 'PAKUR' 'PALAMU' 'RAMGARH' 'RANCHI' 'SAHEBGANJ'
 'SARAIKELA KHARSAWAN' 'SIMDEGA' 'WEST SINGHBHUM' 'BAGALKOT'
 'BANGALORE RURAL' 'BELGAUM' 'BELLARY' 'BENGALURU URBAN' 'BIDAR'
 'CHAMARAJANAGAR' 'CHIKBALLAPUR' 'CHIKMAGALUR' 'CHITRADURGA'
 'DAKSHIN KANNAD' 'DAVANGERE' 'DHARWAD' 'GADAG' 'GULBARGA' 'HASSAN'
 'HAVERI' 'KODAGU' 'KOLAR' 'KOPPAL' 'MANDYA' 'MYSORE' 'RAICHUR'
 'RAMANAGARA' 'SHIMOGA' 'TUMKUR' 'UDUPI' 'UTTAR KANNAD' 'YADGIR'
 'ALAPPUZHA' 'ERNAKULAM' 'IDUKKI' 'KANNUR' 'KASARAGOD' 'KOLLAM' 'KOTTAYAM'
 'KOZHIKODE' 'MALAPPURAM' 'PALAKKAD' 'PATHANAMTHITTA' 'THIRUVANANTHAPURAM'
 'THRISSUR' 'WAYANAD' 'AGAR MALWA' 'ALIRAJPUR' 'ANUPPUR' 'ASHOKNAGAR'
 'BALAGHAT' 'BARWANI' 'BETUL' 'BHIND' 'BHOPAL' 'BURHANPUR' 'CHHATARPUR'
 'CHHINDWARA' 'DAMOH' 'DATIA' 'DEWAS' 'DHAR' 'DINDORI' 'GUNA' 'GWALIOR'
 'HARDA' 'HOSHANGABAD' 'INDORE' 'JABALPUR' 'JHABUA' 'KATNI' 'KHANDWA'
 'KHARGONE' 'MANDLA' 'MANDSAUR' 'MORENA' 'NARSINGHPUR' 'NEEMUCH' 'PANNA'
 'RAISEN' 'RAJGARH' 'RATLAM' 'REWA' 'SAGAR' 'SATNA' 'SEHORE' 'SEONI'
 'SHAHDOL' 'SHAJAPUR' 'SHEOPUR' 'SHIVPURI' 'SIDHI' 'SINGRAULI' 'TIKAMGARH'
 'UJJAIN' 'UMARIA' 'VIDISHA' 'AHMEDNAGAR' 'AKOLA' 'AMRAVATI' 'BEED'
 'BHANDARA' 'BULDHANA' 'CHANDRAPUR' 'DHULE' 'GADCHIROLI' 'GONDIA'
 'HINGOLI' 'JALGAON' 'JALNA' 'KOLHAPUR' 'LATUR' 'MUMBAI' 'NAGPUR' 'NANDED'
 'NANDURBAR' 'NASHIK' 'OSMANABAD' 'PALGHAR' 'PARBHANI' 'PUNE' 'RAIGAD'
 'RATNAGIRI' 'SANGLI' 'SATARA' 'SINDHUDURG' 'SOLAPUR' 'THANE' 'WARDHA'
 'WASHIM' 'YAVATMAL' 'BISHNUPUR' 'CHANDEL' 'CHURACHANDPUR' 'IMPHAL EAST'
 'IMPHAL WEST' 'SENAPATI' 'TAMENGLONG' 'THOUBAL' 'UKHRUL'
 'EAST GARO HILLS' 'EAST JAINTIA HILLS' 'EAST KHASI HILLS'
 'NORTH GARO HILLS' 'RI BHOI' 'SOUTH GARO HILLS' 'SOUTH WEST GARO HILLS'
 'SOUTH WEST KHASI HILLS' 'WEST GARO HILLS' 'WEST JAINTIA HILLS'
 'WEST KHASI HILLS' 'AIZAWL' 'CHAMPHAI' 'KOLASIB' 'LAWNGTLAI' 'LUNGLEI'
 'MAMIT' 'SAIHA' 'SERCHHIP' 'DIMAPUR' 'KIPHIRE' 'KOHIMA' 'LONGLENG'
 'MOKOKCHUNG' 'MON' 'PEREN' 'PHEK' 'TUENSANG' 'WOKHA' 'ZUNHEBOTO' 'ANUGUL'
 'BALANGIR' 'BALESHWAR' 'BARGARH' 'BHADRAK' 'BOUDH' 'CUTTACK' 'DEOGARH'
 'DHENKANAL' 'GAJAPATI' 'GANJAM' 'JAGATSINGHAPUR' 'JAJAPUR' 'JHARSUGUDA'
 'KALAHANDI' 'KANDHAMAL' 'KENDRAPARA' 'KENDUJHAR' 'KHORDHA' 'KORAPUT'
 'MALKANGIRI' 'MAYURBHANJ' 'NABARANGPUR' 'NAYAGARH' 'NUAPADA' 'PURI'
 'RAYAGADA' 'SAMBALPUR' 'SONEPUR' 'SUNDARGARH' 'KARAIKAL' 'MAHE'
 'PONDICHERRY' 'YANAM' 'AMRITSAR' 'BARNALA' 'BATHINDA' 'FARIDKOT'
 'FATEHGARH SAHIB' 'FAZILKA' 'FIROZEPUR' 'GURDASPUR' 'HOSHIARPUR'
 'JALANDHAR' 'KAPURTHALA' 'LUDHIANA' 'MANSA' 'MOGA' 'MUKTSAR' 'NAWANSHAHR'
 'PATHANKOT' 'PATIALA' 'RUPNAGAR' 'S.A.S NAGAR' 'SANGRUR' 'TARN TARAN'
 'AJMER' 'ALWAR' 'BANSWARA' 'BARAN' 'BARMER' 'BHARATPUR' 'BHILWARA'
 'BIKANER' 'BUNDI' 'CHITTORGARH' 'CHURU' 'DAUSA' 'DHOLPUR' 'DUNGARPUR'
 'GANGANAGAR' 'HANUMANGARH' 'JAIPUR' 'JAISALMER' 'JALORE' 'JHALAWAR'
 'JHUNJHUNU' 'JODHPUR' 'KARAULI' 'KOTA' 'NAGAUR' 'PALI' 'PRATAPGARH'
 'RAJSAMAND' 'SAWAI MADHOPUR' 'SIKAR' 'SIROHI' 'TONK' 'UDAIPUR'
 'EAST DISTRICT' 'NORTH DISTRICT' 'SOUTH DISTRICT' 'WEST DISTRICT'
 'ARIYALUR' 'COIMBATORE' 'CUDDALORE' 'DHARMAPURI' 'DINDIGUL' 'ERODE'
 'KANCHIPURAM' 'KANNIYAKUMARI' 'KARUR' 'KRISHNAGIRI' 'MADURAI'
 'NAGAPATTINAM' 'NAMAKKAL' 'PERAMBALUR' 'PUDUKKOTTAI' 'RAMANATHAPURAM'
 'SALEM' 'SIVAGANGA' 'THANJAVUR' 'THE NILGIRIS' 'THENI' 'THIRUVALLUR'
 'THIRUVARUR' 'TIRUCHIRAPPALLI' 'TIRUNELVELI' 'TIRUPPUR' 'TIRUVANNAMALAI'
 'TUTICORIN' 'VELLORE' 'VILLUPURAM' 'VIRUDHUNAGAR' 'ADILABAD' 'HYDERABAD'
 'KARIMNAGAR' 'KHAMMAM' 'MAHBUBNAGAR' 'MEDAK' 'NALGONDA' 'NIZAMABAD'
 'RANGAREDDI' 'WARANGAL' 'DHALAI' 'GOMATI' 'KHOWAI' 'NORTH TRIPURA'
 'SEPAHIJALA' 'SOUTH TRIPURA' 'UNAKOTI' 'WEST TRIPURA' 'AGRA' 'ALIGARH'
 'ALLAHABAD' 'AMBEDKAR NAGAR' 'AMETHI' 'AMROHA' 'AURAIYA' 'AZAMGARH'
 'BAGHPAT' 'BAHRAICH' 'BALLIA' 'BANDA' 'BARABANKI' 'BAREILLY' 'BASTI'
 'BIJNOR' 'BUDAUN' 'BULANDSHAHR' 'CHANDAULI' 'CHITRAKOOT' 'DEORIA' 'ETAH'
 'ETAWAH' 'FAIZABAD' 'FARRUKHABAD' 'FATEHPUR' 'FIROZABAD'
 'GAUTAM BUDDHA NAGAR' 'GHAZIABAD' 'GHAZIPUR' 'GONDA' 'GORAKHPUR' 'HAPUR'
 'HARDOI' 'HATHRAS' 'JALAUN' 'JAUNPUR' 'JHANSI' 'KANNAUJ' 'KANPUR DEHAT'
 'KANPUR NAGAR' 'KASGANJ' 'KAUSHAMBI' 'KHERI' 'KUSHI NAGAR' 'LALITPUR'
 'LUCKNOW' 'MAHARAJGANJ' 'MAHOBA' 'MAINPURI' 'MATHURA' 'MAU' 'MEERUT'
 'MIRZAPUR' 'MORADABAD' 'MUZAFFARNAGAR' 'PILIBHIT' 'RAE BARELI' 'RAMPUR'
 'SAHARANPUR' 'SAMBHAL' 'SANT KABEER NAGAR' 'SANT RAVIDAS NAGAR'
 'SHAHJAHANPUR' 'SHAMLI' 'SHRAVASTI' 'SIDDHARTH NAGAR' 'SITAPUR'
 'SONBHADRA' 'SULTANPUR' 'UNNAO' 'VARANASI' 'ALMORA' 'BAGESHWAR' 'CHAMOLI'
 'CHAMPAWAT' 'DEHRADUN' 'HARIDWAR' 'NAINITAL' 'PAURI GARHWAL'
 'PITHORAGARH' 'RUDRA PRAYAG' 'TEHRI GARHWAL' 'UDAM SINGH NAGAR'
 'UTTAR KASHI' '24 PARAGANAS NORTH' '24 PARAGANAS SOUTH' 'BANKURA'
 'BARDHAMAN' 'BIRBHUM' 'COOCHBEHAR' 'DARJEELING' 'DINAJPUR DAKSHIN'
 'DINAJPUR UTTAR' 'HOOGHLY' 'HOWRAH' 'JALPAIGURI' 'MALDAH'
 'MEDINIPUR EAST' 'MEDINIPUR WEST' 'MURSHIDABAD' 'NADIA' 'PURULIA']
646
In [10]:
[1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
 2011 2012 2013 2014 2015]
In [11]:
Out[11]:
array(['Kharif     ', 'Whole Year ', 'Autumn     ', 'Rabi       ',
       'Summer     ', 'Winter     '], dtype=object)
In [12]:
['Arecanut' 'Other Kharif pulses' 'Rice' 'Banana' 'Cashewnut' 'Coconut '
 'Dry ginger' 'Sugarcane' 'Sweet potato' 'Tapioca' 'Black pepper'
 'Dry chillies' 'other oilseeds' 'Turmeric' 'Maize' 'Moong(Green Gram)'
 'Urad' 'Arhar/Tur' 'Groundnut' 'Sunflower' 'Bajra' 'Castor seed'
 'Cotton(lint)' 'Horse-gram' 'Jowar' 'Korra' 'Ragi' 'Tobacco' 'Gram'
 'Wheat' 'Masoor' 'Sesamum' 'Linseed' 'Safflower' 'Onion'
 'other misc. pulses' 'Samai' 'Small millets' 'Coriander' 'Potato'
 'Other  Rabi pulses' 'Soyabean' 'Beans & Mutter(Vegetable)' 'Bhindi'
 'Brinjal' 'Citrus Fruit' 'Cucumber' 'Grapes' 'Mango' 'Orange'
 'other fibres' 'Other Fresh Fruits' 'Other Vegetables' 'Papaya'
 'Pome Fruit' 'Tomato' 'Rapeseed &Mustard' 'Mesta' 'Cowpea(Lobia)' 'Lemon'
 'Pome Granet' 'Sapota' 'Cabbage' 'Peas  (vegetable)' 'Niger seed'
 'Bottle Gourd' 'Sannhamp' 'Varagu' 'Garlic' 'Ginger' 'Oilseeds total'
 'Pulses total' 'Jute' 'Peas & beans (Pulses)' 'Blackgram' 'Paddy'
 'Pineapple' 'Barley' 'Khesari' 'Guar seed' 'Moth'
 'Other Cereals & Millets' 'Cond-spcs other' 'Turnip' 'Carrot' 'Redish'
 'Arcanut (Processed)' 'Atcanut (Raw)' 'Cashewnut Processed'
 'Cashewnut Raw' 'Cardamom' 'Rubber' 'Bitter Gourd' 'Drum Stick'
 'Jack Fruit' 'Snak Guard' 'Pump Kin' 'Tea' 'Coffee' 'Cauliflower'
 'Other Citrus Fruit' 'Water Melon' 'Total foodgrain' 'Kapas' 'Colocosia'
 'Lentil' 'Bean' 'Jobster' 'Perilla' 'Rajmash Kholar' 'Ricebean (nagadal)'
 'Ash Gourd' 'Beet Root' 'Lab-Lab' 'Ribed Guard' 'Yam' 'Apple' 'Peach'
 'Pear' 'Plums' 'Litchi' 'Ber' 'Other Dry Fruit' 'Jute & mesta']
124
In [13]:
Out[13]:
Crop_Year Area Production
count 246091.000000 2.460910e+05 2.460910e+05
mean 2005.643018 1.200282e+04 5.825034e+05
std 4.952164 5.052340e+04 1.693599e+07
min 1997.000000 4.000000e-02 0.000000e+00
25% 2002.000000 8.000000e+01 9.100000e+01
50% 2006.000000 5.820000e+02 7.880000e+02
75% 2010.000000 4.392000e+03 8.000000e+03
max 2015.000000 8.580100e+06 1.250800e+09
In [59]:
Out[59]:
2.50
1.00
In [16]:
                       State_Name District_Name  Crop_Year       Season  \
0     Andaman and Nicobar Islands      NICOBARS       2000  Kharif        
1     Andaman and Nicobar Islands      NICOBARS       2000  Kharif        
2     Andaman and Nicobar Islands      NICOBARS       2000  Kharif        
3     Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    
4     Andaman and Nicobar Islands      NICOBARS       2000  Whole Year    
...                           ...           ...        ...          ...   
9995            Arunachal Pradesh     CHANGLANG       2002  Rabi          
9996            Arunachal Pradesh     CHANGLANG       2002  Whole Year    
9997            Arunachal Pradesh     CHANGLANG       2002  Whole Year    
9998            Arunachal Pradesh     CHANGLANG       2002  Whole Year    
9999            Arunachal Pradesh     CHANGLANG       2002  Whole Year    

                     Crop    Area  Production  
0                Arecanut  1254.0      2000.0  
1     Other Kharif pulses     2.0         1.0  
2                    Rice   102.0       321.0  
3                  Banana   176.0       641.0  
4               Cashewnut   720.0       165.0  
...                   ...     ...         ...  
9995                Wheat   200.0       320.0  
9996         Dry chillies   320.0       480.0  
9997           Dry ginger   450.0      2475.0  
9998       Oilseeds total  3500.0      2887.0  
9999               Potato    50.0       290.0  

[10000 rows x 7 columns]
Out[16]:
<seaborn.axisgrid.PairGrid at 0x1b3fa4b6990>
In [17]:
In [18]:
C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\777728627.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['Area'])
Out[18]:
<Axes: xlabel='Area', ylabel='Density'>
In [19]:
C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\471295388.py:2: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(dataset['Production'])
Out[19]:
<Axes: xlabel='Production', ylabel='Density'>
In [22]:
In [23]:
           State_Name   District_Name  Crop_Year       Season  \
0       Uttar Pradesh        AZAMGARH       1997  Rabi          
1       Uttar Pradesh          BIJNOR       1997  Kharif        
2       Uttar Pradesh          BIJNOR       1997  Kharif        
3       Uttar Pradesh          BIJNOR       1997  Rabi          
4       Uttar Pradesh          BIJNOR       1997  Rabi          
...               ...             ...        ...          ...   
246086         Odisha  JAGATSINGHAPUR       2015  Summer        
246087         Odisha  JAGATSINGHAPUR       2015  Summer        
246088         Odisha  JAGATSINGHAPUR       2015  Summer        
246089         Odisha      MALKANGIRI       2015  Winter        
246090         Odisha       BALESHWAR       2015  Summer        

                     Crop      Area  Production  
0                 Linseed       9.0         4.0  
1         Total foodgrain  186448.0    505015.0  
2                    Urad    3390.0      2336.0  
3                  Barley      58.0       139.0  
4                    Gram     673.0       578.0  
...                   ...       ...         ...  
246086            Sesamum       7.0         1.9  
246087  Moong(Green Gram)   45996.0     16650.6  
246088         Horse-gram      13.0         3.6  
246089  Moong(Green Gram)      52.0        12.2  
246090          Groundnut    2079.0      2490.6  

[246091 rows x 7 columns]
In [24]:
                                                                     Districts  \
State_Name                                                                       
Andaman and Nicobar Islands  NICOBARS, NORTH AND MIDDLE ANDAMAN, SOUTH ANDA...   
Andhra Pradesh               ANANTAPUR, CHITTOOR, EAST GODAVARI, GUNTUR, KA...   
Arunachal Pradesh            ANJAW, CHANGLANG, DIBANG VALLEY, EAST KAMENG, ...   
Assam                        BAKSA, BARPETA, BONGAIGAON, CACHAR, CHIRANG, D...   
Bihar                        ARARIA, ARWAL, AURANGABAD, BANKA, BEGUSARAI, B...   
Chandigarh                                                          CHANDIGARH   
Chhattisgarh                 BALOD, BALODA BAZAR, BALRAMPUR, BASTAR, BEMETA...   
Dadra and Nagar Haveli                                  DADRA AND NAGAR HAVELI   
Goa                                                       NORTH GOA, SOUTH GOA   
Gujarat                      AHMADABAD, AMRELI, ANAND, BANAS KANTHA, BHARUC...   
Haryana                      AMBALA, BHIWANI, FARIDABAD, FATEHABAD, GURGAON...   
Himachal Pradesh             BILASPUR, CHAMBA, HAMIRPUR, KANGRA, KINNAUR, K...   
Jammu and Kashmir            ANANTNAG, BADGAM, BANDIPORA, BARAMULLA, DODA, ...   
Jharkhand                    BOKARO, CHATRA, DEOGHAR, DHANBAD, DUMKA, EAST ...   
Karnataka                    BAGALKOT, BANGALORE RURAL, BELGAUM, BELLARY, B...   
Kerala                       ALAPPUZHA, ERNAKULAM, IDUKKI, KANNUR, KASARAGO...   
Madhya Pradesh               AGAR MALWA, ALIRAJPUR, ANUPPUR, ASHOKNAGAR, BA...   
Maharashtra                  AHMEDNAGAR, AKOLA, AMRAVATI, AURANGABAD, BEED,...   
Manipur                      BISHNUPUR, CHANDEL, CHURACHANDPUR, IMPHAL EAST...   
Meghalaya                    EAST GARO HILLS, EAST JAINTIA HILLS, EAST KHAS...   
Mizoram                      AIZAWL, CHAMPHAI, KOLASIB, LAWNGTLAI, LUNGLEI,...   
Nagaland                     DIMAPUR, KIPHIRE, KOHIMA, LONGLENG, MOKOKCHUNG...   
Odisha                       ANUGUL, BALANGIR, BALESHWAR, BARGARH, BHADRAK,...   
Puducherry                                  KARAIKAL, MAHE, PONDICHERRY, YANAM   
Punjab                       AMRITSAR, BARNALA, BATHINDA, FARIDKOT, FATEHGA...   
Rajasthan                    AJMER, ALWAR, BANSWARA, BARAN, BARMER, BHARATP...   
Sikkim                       EAST DISTRICT, NORTH DISTRICT, SOUTH DISTRICT,...   
Tamil Nadu                   ARIYALUR, COIMBATORE, CUDDALORE, DHARMAPURI, D...   
Telangana                    ADILABAD, HYDERABAD, KARIMNAGAR, KHAMMAM, MAHB...   
Tripura                      DHALAI, GOMATI, KHOWAI, NORTH TRIPURA, SEPAHIJ...   
Uttar Pradesh                AGRA, ALIGARH, ALLAHABAD, AMBEDKAR NAGAR, AMET...   
Uttarakhand                  ALMORA, BAGESHWAR, CHAMOLI, CHAMPAWAT, DEHRADU...   
West Bengal                  24 PARAGANAS NORTH, 24 PARAGANAS SOUTH, BANKUR...   

                             District_Count  
State_Name                                   
Andaman and Nicobar Islands               3  
Andhra Pradesh                           13  
Arunachal Pradesh                        18  
Assam                                    27  
Bihar                                    38  
Chandigarh                                1  
Chhattisgarh                             27  
Dadra and Nagar Haveli                    1  
Goa                                       2  
Gujarat                                  26  
Haryana                                  21  
Himachal Pradesh                         12  
Jammu and Kashmir                        22  
Jharkhand                                24  
Karnataka                                30  
Kerala                                   14  
Madhya Pradesh                           51  
Maharashtra                              35  
Manipur                                   9  
Meghalaya                                11  
Mizoram                                   8  
Nagaland                                 11  
Odisha                                   30  
Puducherry                                4  
Punjab                                   22  
Rajasthan                                33  
Sikkim                                    4  
Tamil Nadu                               31  
Telangana                                10  
Tripura                                   8  
Uttar Pradesh                            75  
Uttarakhand                              13  
West Bengal                              18  

C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\506792529.py:8: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  district_info = new_df.groupby('State_Name').apply(get_district_info)
In [25]:
Out[25]:
<Axes: xlabel='Season'>
In [26]:
Season               Autumn       Kharif       Rabi         Summer       \
Crop                                                                      
Apple                          0            0            0            0   
Arcanut (Processed)            0            0            0            0   
Arecanut                       0           16          118            0   
Arhar/Tur                     18         6798          447           28   
Ash Gourd                      0            0            0            0   
...                          ...          ...          ...          ...   
Wheat                          0            7         7520          300   
Yam                            0            0            0            0   
other fibres                   0            0            0            0   
other misc. pulses             0           29           41            0   
other oilseeds                 0          248          303            0   

Season               Whole Year   Winter       
Crop                                           
Apple                          4            0  
Arcanut (Processed)           20            0  
Arecanut                    1443            0  
Arhar/Tur                    256           31  
Ash Gourd                     44            0  
...                          ...          ...  
Wheat                         55           17  
Yam                           36            0  
other fibres                  10            0  
other misc. pulses             0            0  
other oilseeds                82            0  

[124 rows x 6 columns]
In [27]:
The combination of season and year with the highest production is ('Whole Year ', 2011) with a total production of 13878425797.888565
In [28]:
Out[28]:
Season
Autumn           9641
Kharif         240975
Rabi             6253
Summer          19955
Whole Year     197646
Winter          73569
Name: Production, dtype: int64
In [29]:
        Season  Max_Crop_Year
0  Autumn                1998
1  Kharif                2014
2  Rabi                  1997
3  Summer                1998
4  Whole Year            2011
5  Winter                2002

C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\1800136364.py:8: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  max_years = new_df.groupby('Season').apply(get_max_year)
In [33]:
In [34]:
        Season  Max_Crop_Year  Production
0  Autumn                1998        9641
1  Kharif                2014      240975
2  Rabi                  1997        6253
3  Summer                1998       19955
4  Whole Year            2011      197646
5  Winter                2002       73569
In [35]:
        Season Max_State_Names
0  Autumn               Odisha
1  Kharif        Uttar Pradesh
2  Rabi            West Bengal
3  Summer          West Bengal
4  Whole Year       Tamil Nadu
5  Winter          West Bengal

C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\2683243550.py:9: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  max_state = new_df.groupby('Season').apply(get_max_state)
In [36]:
        Season  Max_Crop_Year Max_State_Names  Production
0  Autumn                1998          Odisha        9641
1  Kharif                2014   Uttar Pradesh      240975
2  Rabi                  1997     West Bengal        6253
3  Summer                1998     West Bengal       19955
4  Whole Year            2011      Tamil Nadu      197646
5  Winter                2002     West Bengal       73569
In [37]:
Interactive 3D Line Chart - Prroduction of crops in Different Seasons
In [38]:
        Season  District_Names
0  Autumn              DEOGARH
1  Kharif                KHERI
2  Rabi                  NADIA
3  Summer            BARDHAMAN
4  Whole Year       COIMBATORE
5  Winter       MEDINIPUR WEST

C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\2582194667.py:7: DeprecationWarning:

DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
In [39]:
        Season  Max_Crop_Year Max_State_Names  Production  District_Names
0  Autumn                1998          Odisha        9641         DEOGARH
1  Kharif                2014   Uttar Pradesh      240975           KHERI
2  Rabi                  1997     West Bengal        6253           NADIA
3  Summer                1998     West Bengal       19955       BARDHAMAN
4  Whole Year            2011      Tamil Nadu      197646      COIMBATORE
5  Winter                2002     West Bengal       73569  MEDINIPUR WEST
In [40]:
In [41]:
Crops that grow in all seasons:
Arhar/Tur
Banana
Cotton(lint)
Dry chillies
Dry ginger
Groundnut
Maize
Moong(Green Gram)
Onion
Peas & beans (Pulses)
Potato
Ragi
Rice
Sesamum
Sugarcane
Turmeric
Urad
In [42]:
Crop: Apple
Seasons:
Whole Year : 4

Crop: Arcanut (Processed)
Seasons:
Whole Year : 20

Crop: Arecanut
Seasons:
Kharif     : 16
Rabi       : 118
Whole Year : 1443

Crop: Arhar/Tur
Seasons:
Autumn     : 18
Kharif     : 6798
Rabi       : 447
Summer     : 28
Whole Year : 256
Winter     : 31

Crop: Ash Gourd
Seasons:
Whole Year : 44

Crop: Atcanut (Raw)
Seasons:
Whole Year : 20

Crop: Bajra
Seasons:
Kharif     : 4672
Rabi       : 264
Summer     : 331
Whole Year : 160

Crop: Banana
Seasons:
Autumn     : 6
Kharif     : 131
Rabi       : 60
Summer     : 190
Whole Year : 2815
Winter     : 7

Crop: Barley
Seasons:
Kharif     : 134
Rabi       : 4017
Whole Year : 48

Crop: Bean
Seasons:
Kharif     : 20

Crop: Beans & Mutter(Vegetable)
Seasons:
Whole Year : 167

Crop: Beet Root
Seasons:
Whole Year : 16

Crop: Ber
Seasons:
Whole Year : 11

Crop: Bhindi
Seasons:
Whole Year : 236

Crop: Bitter Gourd
Seasons:
Whole Year : 92

Crop: Black pepper
Seasons:
Kharif     : 15
Rabi       : 118
Whole Year : 801

Crop: Blackgram
Seasons:
Kharif     : 42
Rabi       : 57
Whole Year : 19

Crop: Bottle Gourd
Seasons:
Whole Year : 84

Crop: Brinjal
Seasons:
Kharif     : 43
Rabi       : 43
Summer     : 5
Whole Year : 295

Crop: Cabbage
Seasons:
Kharif     : 29
Rabi       : 32
Whole Year : 144

Crop: Cardamom
Seasons:
Kharif     : 17
Whole Year : 410

Crop: Carrot
Seasons:
Whole Year : 28

Crop: Cashewnut
Seasons:
Kharif     : 57
Rabi       : 3
Whole Year : 1021

Crop: Cashewnut Processed
Seasons:
Whole Year : 21

Crop: Cashewnut Raw
Seasons:
Whole Year : 35

Crop: Castor seed
Seasons:
Kharif     : 2957
Rabi       : 209
Whole Year : 210

Crop: Cauliflower
Seasons:
Whole Year : 122

Crop: Citrus Fruit
Seasons:
Whole Year : 301

Crop: Coconut 
Seasons:
Kharif     : 17
Whole Year : 1968

Crop: Coffee
Seasons:
Whole Year : 6

Crop: Colocosia
Seasons:
Kharif     : 11

Crop: Cond-spcs other
Seasons:
Kharif     : 10
Rabi       : 8

Crop: Coriander
Seasons:
Kharif     : 8
Rabi       : 530
Whole Year : 2830
Winter     : 1

Crop: Cotton(lint)
Seasons:
Autumn     : 1
Kharif     : 3974
Rabi       : 138
Summer     : 42
Whole Year : 362
Winter     : 1

Crop: Cowpea(Lobia)
Seasons:
Kharif     : 241
Rabi       : 246
Summer     : 66
Whole Year : 34

Crop: Cucumber
Seasons:
Whole Year : 93

Crop: Drum Stick
Seasons:
Whole Year : 112

Crop: Dry chillies
Seasons:
Autumn     : 41
Kharif     : 1281
Rabi       : 816
Summer     : 241
Whole Year : 4104
Winter     : 6

Crop: Dry ginger
Seasons:
Autumn     : 6
Kharif     : 1372
Rabi       : 5
Summer     : 5
Whole Year : 1615
Winter     : 5

Crop: Garlic
Seasons:
Kharif     : 21
Rabi       : 544
Whole Year : 2724

Crop: Ginger
Seasons:
Kharif     : 76
Rabi       : 109
Whole Year : 56

Crop: Gram
Seasons:
Kharif     : 99
Rabi       : 7074
Whole Year : 173
Winter     : 15

Crop: Grapes
Seasons:
Kharif     : 12
Whole Year : 117

Crop: Groundnut
Seasons:
Autumn     : 379
Kharif     : 5359
Rabi       : 1116
Summer     : 1446
Whole Year : 276
Winter     : 258

Crop: Guar seed
Seasons:
Kharif     : 529
Whole Year : 766

Crop: Horse-gram
Seasons:
Kharif     : 2104
Rabi       : 1170
Summer     : 79
Whole Year : 136
Winter     : 413

Crop: Jack Fruit
Seasons:
Whole Year : 112

Crop: Jobster
Seasons:
Kharif     : 9

Crop: Jowar
Seasons:
Autumn     : 3
Kharif     : 5044
Rabi       : 1592
Summer     : 240
Whole Year : 186

Crop: Jute
Seasons:
Autumn     : 114
Kharif     : 1329
Rabi       : 9
Summer     : 1

Crop: Jute & mesta
Seasons:
Kharif     : 12
Whole Year : 8

Crop: Kapas
Seasons:
Kharif     : 8
Whole Year : 4

Crop: Khesari
Seasons:
Kharif     : 1
Rabi       : 1329
Whole Year : 30

Crop: Korra
Seasons:
Kharif     : 117
Rabi       : 9

Crop: Lab-Lab
Seasons:
Whole Year : 48

Crop: Lemon
Seasons:
Kharif     : 39

Crop: Lentil
Seasons:
Rabi       : 31

Crop: Linseed
Seasons:
Kharif     : 7
Rabi       : 4335
Whole Year : 63

Crop: Litchi
Seasons:
Whole Year : 6

Crop: Maize
Seasons:
Autumn     : 934
Kharif     : 7320
Rabi       : 2735
Summer     : 2522
Whole Year : 240
Winter     : 196

Crop: Mango
Seasons:
Kharif     : 55
Whole Year : 394

Crop: Masoor
Seasons:
Kharif     : 80
Rabi       : 4096
Whole Year : 48

Crop: Mesta
Seasons:
Kharif     : 1705
Rabi       : 3
Whole Year : 79

Crop: Moong(Green Gram)
Seasons:
Autumn     : 190
Kharif     : 5577
Rabi       : 1943
Summer     : 2064
Whole Year : 209
Winter     : 335

Crop: Moth
Seasons:
Kharif     : 856
Rabi       : 1
Whole Year : 21

Crop: Niger seed
Seasons:
Kharif     : 1932
Rabi       : 71
Whole Year : 51
Winter     : 16

Crop: Oilseeds total
Seasons:
Kharif     : 61
Rabi       : 29
Whole Year : 336

Crop: Onion
Seasons:
Autumn     : 4
Kharif     : 627
Rabi       : 1539
Summer     : 855
Whole Year : 3985
Winter     : 2

Crop: Orange
Seasons:
Kharif     : 104
Whole Year : 167

Crop: Other  Rabi pulses
Seasons:
Rabi       : 3175
Summer     : 5

Crop: Other Cereals & Millets
Seasons:
Kharif     : 358
Rabi       : 270

Crop: Other Citrus Fruit
Seasons:
Whole Year : 69

Crop: Other Dry Fruit
Seasons:
Whole Year : 1

Crop: Other Fresh Fruits
Seasons:
Whole Year : 410

Crop: Other Kharif pulses
Seasons:
Kharif     : 3651
Rabi       : 8

Crop: Other Vegetables
Seasons:
Whole Year : 381

Crop: Paddy
Seasons:
Autumn     : 138
Kharif     : 61
Rabi       : 13
Summer     : 129
Winter     : 138

Crop: Papaya
Seasons:
Kharif     : 147
Rabi       : 4
Whole Year : 332

Crop: Peach
Seasons:
Whole Year : 4

Crop: Pear
Seasons:
Whole Year : 6

Crop: Peas  (vegetable)
Seasons:
Whole Year : 11

Crop: Peas & beans (Pulses)
Seasons:
Autumn     : 6
Kharif     : 352
Rabi       : 3965
Summer     : 61
Whole Year : 135
Winter     : 5

Crop: Perilla
Seasons:
Kharif     : 9

Crop: Pineapple
Seasons:
Rabi       : 108
Whole Year : 139

Crop: Plums
Seasons:
Whole Year : 6

Crop: Pome Fruit
Seasons:
Whole Year : 297

Crop: Pome Granet
Seasons:
Kharif     : 21
Whole Year : 45

Crop: Potato
Seasons:
Autumn     : 6
Kharif     : 569
Rabi       : 1604
Summer     : 97
Whole Year : 4165
Winter     : 490

Crop: Pulses total
Seasons:
Kharif     : 70
Rabi       : 27
Summer     : 2
Whole Year : 167

Crop: Pump Kin
Seasons:
Whole Year : 93

Crop: Ragi
Seasons:
Autumn     : 375
Kharif     : 2448
Rabi       : 598
Summer     : 361
Whole Year : 180
Winter     : 183

Crop: Rajmash Kholar
Seasons:
Kharif     : 9
Rabi       : 9

Crop: Rapeseed &Mustard
Seasons:
Kharif     : 110
Rabi       : 6978
Whole Year : 120
Winter     : 384

Crop: Redish
Seasons:
Whole Year : 61

Crop: Ribed Guard
Seasons:
Whole Year : 38

Crop: Rice
Seasons:
Autumn     : 2091
Kharif     : 6878
Rabi       : 797
Summer     : 2956
Whole Year : 128
Winter     : 2254

Crop: Ricebean (nagadal)
Seasons:
Kharif     : 10

Crop: Rubber
Seasons:
Whole Year : 29

Crop: Safflower
Seasons:
Kharif     : 14
Rabi       : 1301
Whole Year : 20

Crop: Samai
Seasons:
Kharif     : 67
Rabi       : 20

Crop: Sannhamp
Seasons:
Autumn     : 6
Kharif     : 916
Rabi       : 2
Whole Year : 1468
Winter     : 3

Crop: Sapota
Seasons:
Kharif     : 39

Crop: Sesamum
Seasons:
Autumn     : 312
Kharif     : 6493
Rabi       : 539
Summer     : 552
Whole Year : 712
Winter     : 438

Crop: Small millets
Seasons:
Autumn     : 9
Kharif     : 4155
Rabi       : 311
Summer     : 9
Whole Year : 168

Crop: Snak Guard
Seasons:
Whole Year : 82

Crop: Soyabean
Seasons:
Autumn     : 5
Kharif     : 3103
Rabi       : 41
Whole Year : 61
Winter     : 2

Crop: Sugarcane
Seasons:
Autumn     : 6
Kharif     : 1159
Rabi       : 13
Summer     : 2
Whole Year : 6297
Winter     : 444

Crop: Sunflower
Seasons:
Kharif     : 2577
Rabi       : 1648
Summer     : 1010
Whole Year : 334
Winter     : 2

Crop: Sweet potato
Seasons:
Autumn     : 1
Kharif     : 753
Rabi       : 190
Whole Year : 3253
Winter     : 1

Crop: Tapioca
Seasons:
Autumn     : 1
Kharif     : 294
Rabi       : 9
Whole Year : 1282

Crop: Tea
Seasons:
Kharif     : 8
Whole Year : 54

Crop: Tobacco
Seasons:
Kharif     : 177
Rabi       : 328
Summer     : 78
Whole Year : 2115

Crop: Tomato
Seasons:
Kharif     : 39
Rabi       : 39
Whole Year : 290

Crop: Total foodgrain
Seasons:
Kharif     : 125
Rabi       : 13
Summer     : 5
Whole Year : 45

Crop: Turmeric
Seasons:
Autumn     : 6
Kharif     : 135
Rabi       : 382
Summer     : 1
Whole Year : 3676
Winter     : 2

Crop: Turnip
Seasons:
Whole Year : 8

Crop: Urad
Seasons:
Autumn     : 291
Kharif     : 5947
Rabi       : 1872
Summer     : 1158
Whole Year : 173
Winter     : 409

Crop: Varagu
Seasons:
Kharif     : 45
Rabi       : 13

Crop: Water Melon
Seasons:
Whole Year : 85

Crop: Wheat
Seasons:
Kharif     : 7
Rabi       : 7520
Summer     : 300
Whole Year : 55
Winter     : 17

Crop: Yam
Seasons:
Whole Year : 36

Crop: other fibres
Seasons:
Whole Year : 10

Crop: other misc. pulses
Seasons:
Kharif     : 29
Rabi       : 41

Crop: other oilseeds
Seasons:
Kharif     : 248
Rabi       : 303
Whole Year : 82
In [90]:
Season               Autumn  Kharif  Rabi  Summer  Whole Year  Winter
Crop                                                                 
Apple                     0       0     0       0           4       0
Arcanut (Processed)       0       0     0       0          20       0
Arecanut                  0      16   118       0        1443       0
Arhar/Tur                18    6798   447      28         256      31
Ash Gourd                 0       0     0       0          44       0
...                     ...     ...   ...     ...         ...     ...
Wheat                     0       7  7520     300          55      17
Yam                       0       0     0       0          36       0
other fibres              0       0     0       0          10       0
other misc. pulses        0      29    41       0           0       0
other oilseeds            0     248   303       0          82       0

[124 rows x 6 columns]
In [43]:
Crops grown in autumn season:
Index(['Arhar/Tur', 'Banana', 'Cotton(lint)', 'Dry chillies', 'Dry ginger',
       'Groundnut', 'Jowar', 'Jute', 'Maize', 'Moong(Green Gram)', 'Onion',
       'Paddy', 'Peas & beans (Pulses)', 'Potato', 'Ragi', 'Rice', 'Sannhamp',
       'Sesamum', 'Small millets', 'Soyabean', 'Sugarcane', 'Sweet potato',
       'Tapioca', 'Turmeric', 'Urad'],
      dtype='object', name='Crop')
In [44]:
Maximum produced crop for each season:
Season
Autumn             Rice
Kharif            Maize
Rabi              Wheat
Summer             Rice
Whole Year    Sugarcane
Winter             Rice
dtype: object
In [45]:
Minimum produced crop for each season (excluding crops with production equal to 0):
Season
Autumn           Cotton(lint)
Kharif                Khesari
Rabi                     Moth
Summer                   Jute
Whole Year    Other Dry Fruit
Winter              Coriander
dtype: object
In [46]:
In [47]:
Crop with the least and no production across all seasons: Apple
In [48]:
        Season            Crop
0  Autumn                 Ragi
1  Kharif            Sugarcane
2  Rabi         Oilseeds total
3  Summer                 Rice
4  Whole Year         Coconut 
5  Winter               Potato

C:\Users\ASUS\AppData\Local\Temp\ipykernel_16060\130618607.py:7: DeprecationWarning:

DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
In [49]:
        Season  Max_Crop_Year Max_State_Names  Production  District_Names  \
0  Autumn                1998          Odisha        9641         DEOGARH   
1  Kharif                2014   Uttar Pradesh      240975           KHERI   
2  Rabi                  1997     West Bengal        6253           NADIA   
3  Summer                1998     West Bengal       19955       BARDHAMAN   
4  Whole Year            2011      Tamil Nadu      197646      COIMBATORE   
5  Winter                2002     West Bengal       73569  MEDINIPUR WEST   

             Crop  
0            Ragi  
1       Sugarcane  
2  Oilseeds total  
3            Rice  
4        Coconut   
5          Potato
In [50]:
Highest produced crop for each year:
    Crop_Year       Crop       Season    Production
0        1997  Sugarcane  Whole Year   3.715800e+07
1        1998   Coconut   Whole Year   9.990000e+08
2        1999   Coconut   Whole Year   1.059000e+09
3        2000   Coconut   Whole Year   9.030000e+08
4        2001   Coconut   Whole Year   8.950000e+08
5        2002   Coconut   Whole Year   8.880000e+08
6        2003   Coconut   Whole Year   8.980000e+08
7        2004   Coconut   Whole Year   9.000000e+08
8        2005   Coconut   Whole Year   8.630000e+08
9        2006   Coconut   Whole Year   8.590000e+08
10       2007   Coconut   Whole Year   8.510000e+08
11       2008   Coconut   Whole Year   9.520000e+08
12       2009   Coconut   Whole Year   1.063000e+09
13       2010   Coconut   Whole Year   9.160530e+08
14       2011   Coconut   Whole Year   1.250800e+09
15       2012   Coconut   Whole Year   1.125000e+09
16       2013   Coconut   Whole Year   1.212000e+09
17       2014   Coconut   Whole Year   1.001000e+09
18       2015       Rice  Autumn       5.825034e+05
In [118]: